Eurographics Digital Library

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Recent Submissions

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Semantic Stylization and Shading via Segmentation Atlas utilizing Deep Learning Approaches
(The Eurographics Association, 2024) Sinha, Saptarshi Neil; Kühn, Paul Julius; Rojtberg, Pavel; Graf, Holger; Kuijper, Arjan; Weinmann, Michael; Caputo, Ariel; Garro, Valeria; Giachetti, Andrea; Castellani, Umberto; Dulecha, Tinsae Gebrechristos
We present a novel hybrid approach for semantic stylization of surface materials of 3D models while preserving shading. Based on a hybrid approach that builds on directly applying style transfer on the object surface obtained by learning-based or traditional methods such as 3D scanners or structured light systems, thereby overcoming artifacts like halos, ghosting or lacking quality of the geometric representation produced by other 3D stylization methods. For this purpose, our methods involves (i) the initial generation of a segmentation map parameterized over the object surface inferred based on a deep-learning-based foundation model to guide the stylization and shading of different regions of the 3D model, and (ii) a subsequent 2D style transfer that allows the exchange or stylization of surface materials in high quality. By delivering high-quality semantic perceptive reconstructions in a shorter timeframe than current approaches using manual 3D segmentation and stylization, our approach holds significant potential for various application scenarios including creative design, architecture and cultural heritage.
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Meshtrics: Objective Quality Assessment of Textured 3D Meshes for 3D Reconstruction
(The Eurographics Association, 2024) Madeira, Tiago; Oliveira, Miguel; Dias, Paulo; Caputo, Ariel; Garro, Valeria; Giachetti, Andrea; Castellani, Umberto; Dulecha, Tinsae Gebrechristos
Abstract In the context of 3D reconstruction, the pursuit of photorealistic models requires precise, objective quality evaluation methods. In this work, we investigate several potential objective metrics for the quality assessment of textured 3D meshes by evaluating their correlation with human perception of visual quality. We conduct experiments using a publicly available, subjectively-rated database of textured 3D meshes containing various types of geometry and texture distortions. Based on these experiments, we discuss the characteristics and limitations of the evaluated metrics. Notably, image-based metrics demonstrated the strongest correlation with subjective scores in most tested scenarios, suggesting that 2D image metrics are reliable predictors of 3D model visual quality. We then introduce a framework designed to facilitate the analysis of various characteristics of 3D models and their fidelity, with a particular focus on image-based metrics leveraging photographs of real-world environments as reference. Our toolkit streamlines the generation of renders and the application of quality metrics, enabling manual annotation in 2D and 3D spaces, while incorporating an automatic alignment refinement step for precise registration of reference photographs. We evaluate the proposed approach using a dataset generated through the 3D reconstruction of a complex indoor environment. Our experiments support the efficacy of the solution in benchmarking 3D reconstruction results, enabling timely informed adjustments to the reconstruction methodology. Source code is available at https://github.com/tiagomfmadeira/Meshtrics.
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Advancing Environmental Modeling with Unstructured Meshes: Current Research and Development
(The Eurographics Association, 2024) Miola, Marianna; Cabiddu, Daniela; Mortara, Michela; Pittaluga, Simone; Sorgente, Tommaso; Zuccolini, Marino Vetuschi; Caputo, Ariel; Garro, Valeria; Giachetti, Andrea; Castellani, Umberto; Dulecha, Tinsae Gebrechristos
Modeling the distribution of environmental variables across spatial domains presents significant challenges. Geostatistics offers a robust set of tools for accurately predicting values and associated uncertainties at unsampled locations, accounting for spatial correlations. However, these tools are often constrained by their reliance on structured domain representations, limiting their flexibility in modeling complex or irregular structures. By exploring the use of unstructured meshes, we can achieve a more efficient and accurate representation of localized phenomena, thereby enhancing our ability to model spatial patterns. Our current efforts are focused on integrating unstructured meshes into the geostatistical modeling pipeline, encompassing everything from mesh generation (and possibly refinement) to their application in stochastic simulation and the segmentation of the domain into regions where the distribution of variables is homogeneous. Preliminary results are promising, demonstrating the potentialities of this innovative approach.
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The use of Virtual Reality in preserving and reactivating immersive audio art installations: the case of Dissonanze Circolari by Roberto Taroni
(The Eurographics Association, 2024) Russo, Alessandro; Fayyaz, Nikoo; Franceschini, Andrea; Caputo, Ariel; Garro, Valeria; Giachetti, Andrea; Castellani, Umberto; Dulecha, Tinsae Gebrechristos
Interactive multimedia artworks pose unique challenges for their preservation, such as the obsolescence of original components, software, and playback devices, and other issues related to their interactive and time-based nature. The Centro di Sonologia Computazionale (CSC) of the University of Padova developed the Multilevel Dynamic Preservation (MDP) model, which aims at ensuring the long-term preservation of multimedia artworks by treating them as dynamic objects. Reactivation is a fundamental step for allowing their preservation, and, among various reactivation strategies, Virtual Reality (VR) provides a unique opportunity to recreate the immersive experience while still maintaining the concept of the original artwork. The CSC started to work together with Italian artist Roberto Taroni, a central figure in the experimental scenario, who often combined music and visual arts in his works. This contribution concerns the reactivation in VR of Roberto Taroni's artwork ''Dissonanze Circolari'' from 1999. This installation featured a room with 16 speakers, each one playing a fragment of Beethoven's piano performance, Op.111, executed by different musicians, creating a dissonance-based immersive experience. The reactivation was carried out using the documentation provided by the artist and the audio samples from the original installation. The VR environment was created using the game engine Unreal Engine 5. This reactivation approach allows to maximize access to the artwork, providing new information for curators, scholars, and art enthusiasts.
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Surface Reconstruction from Silhouette and Laser Scanners as a Positive-Unlabeled Learning Problem
(The Eurographics Association, 2024) Gottardo, Mario; Pistellato, Mara; Bergamasco, Filippo; Caputo, Ariel; Garro, Valeria; Giachetti, Andrea; Castellani, Umberto; Dulecha, Tinsae Gebrechristos
Typical 3D reconstruction pipelines employ a combination of line-laser scanners and robotic actuators to produce a point cloud and then proceed with surface reconstruction. In this work we propose a new technique to learn an Implicit Neural Representation (INR) of a 3D shape S without directly observing points on its surface. We just assume being able to determine whether a 3D point is exterior to S (e.g. observing if the projection falls outside the silhouette or detecting on which side of the laser line the point is). In this setting, we cast the reconstruction process as a Positive-Unlabelled learning problem where sparse 3D points, sampled according to a distribution depending on the INR's local gradient, have to be classified as being interior or exterior to S. These points, are used to train the INR in an iterative way so that its zero-crossing converges to the boundary of the shape. Preliminary experiments performed on a synthetic dataset demonstrates the advantages of the approach.